/abcpy

ABCpy package

Primary LanguagePythonBSD 3-Clause Clear LicenseBSD-3-Clause-Clear

ABCpy Documentation Status Build Status

ABCpy is a scientific library written in Python for Bayesian uncertainty quantification in absence of likelihood function, which parallelizes existing approaximate Bayesian computation (ABC) algorithms and other likelihood-free inference schemes. It presently includes:

  • RejectionABC
  • PMCABC (Population Monte Carlo ABC)
  • SMCABC (Sequential Monte Carlo ABC)
  • RSMCABC (Replenishment SMC-ABC)
  • APMCABC (Adaptive Population Monte Carlo ABC)
  • SABC (Simulated Annealing ABC)
  • ABCsubsim (ABC using subset simulation)
  • PMC (Population Monte Carlo) using approximations of likelihood functions
  • Random Forest Model Selection Scheme
  • Semi-automatic summary selection

ABCpy addresses the needs of domain scientists and data scientists by providing

  • a fully modularized framework that is easy to use and easy to extend,
  • a quick way to integrate your generative model into the framework (from C++, R etc.) and
  • a non-intrusive, user-friendly way to parallelize inference computations (for your laptop to clusters, supercomputers and AWS)
  • an intuitive way to perform inference on hierarchical models or more generally on Bayesian networks

Documentation

For more information, check out the

Further, we provide a collection of models for which ABCpy has been applied successfully. This is a good place to look at more complicated inference setups.

Author

ABCpy was written by Ritabrata Dutta, Università della svizzera italiana and Marcel Schoengens, CSCS, ETH Zurich, and we're actively developing it. Please feel free to submit any bugs or feature requests. We'd also love to hear about your experiences with ABCpy in general. Drop us an email!

We want to thank Prof. Antonietta Mira, Università della svizzera italiana, and Prof. Jukka-Pekka Onnela, Harvard University for helpful contributions and advice; Avinash Ummadisinghu and Nicole Widmern respectively for developing dynamic-MPI backend and making ABCpy suitbale for hierarchical models; and finally CSCS (Swiss National Super Computing Center) for their generous support.

Citation

There is a paper in the proceedings of the 2017 PASC conference. We would appreciate a citation.

@inproceedings{Dutta:2017:AUE:3093172.3093233,
 author = {Dutta, Ritabrata and Schoengens, Marcel and Onnela, Jukka-Pekka and Mira, Antonietta},
 title = {ABCpy: A User-Friendly, Extensible, and Parallel Library for Approximate Bayesian Computation},
 booktitle = {Proceedings of the Platform for Advanced Scientific Computing Conference},
 series = {PASC '17},
 year = {2017},
 isbn = {978-1-4503-5062-4},
 location = {Lugano, Switzerland},
 pages = {8:1--8:9},
 articleno = {8},
 numpages = {9},
 url = {http://doi.acm.org/10.1145/3093172.3093233},
 doi = {10.1145/3093172.3093233},
 acmid = {3093233},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {ABC, Library, Parallel, Spark},
}

Other Refernces

Publications in which ABCpy was applied:

  • R. Dutta, M. Schoengens, A. Ummadisingu, J. P. Onnela, A. Mira, "ABCpy: A High-Performance Computing Perspective to Approximate Bayesian Computation", 2017, arXiv:1711.04694

  • R. Dutta, J. P. Onnela, A. Mira, "Bayesian Inference of Spreading Processes on Networks", 2017, arXiv:1709.08862

  • R. Dutta, B. Chopard, J. Lätt, F. Dubois, K. Zouaoui Boudjeltia and A. Mira, "Parameter Estimation of Platelets Deposition: Approximate Bayesian Computation with High Performance Computing", 2017, arXiv:1710.01054

  • A. Ebert, R. Dutta, P. Wu, K. Mengersen and A. Mira, "Likelihood-Free Parameter Estimation for Dynamic Queueing Networks", 2018, arXiv:1804.02526

  • R. Dutta, Z. Faidon Brotzakis and A. Mira, "Bayesian Calibration of Force-fields from Experimental Data: TIP4P Water", 2018, arXiv:1804.02742

License

ABCpy is published under the BSD 3-clause license, see here.

Contribute

You are very welcome to contribute to ABCpy.

If you want to contribute code, there are a few things to consider: